{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"09_TF_Recurrent_Neural_Networks","provenance":[{"file_id":"https://github.com/madewithml/lessons/blob/master/notebooks/02_Basics/09_Recurrent_Neural_Networks/09_TF_Recurrent_Neural_Networks.ipynb","timestamp":1584546229452},{"file_id":"https://github.com/madewithml/lessons/blob/master/notebooks/02_Basics/12_Recurrent_Neural_Networks.ipynb","timestamp":1583255247211}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"eTdCMVl9YAXw","colab_type":"text"},"source":["# Recurrent Neural Networks (RNN)\n","\n","In this lesson we will learn how to process sequential data (sentences, time-series, etc.) with recurrent neural networks (RNNs). "]},{"cell_type":"markdown","metadata":{"id":"xuabAj4PYj57","colab_type":"text"},"source":["<div align=\"left\">\n","<a href=\"https://github.com/madewithml/lessons/blob/master/notebooks/02_Basics/09_Recurrent_Neural_Networks/09_TF_Recurrent_Neural_Networks.ipynb\" role=\"button\"><img class=\"notebook-badge-image\" src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/madewithml/lessons/blob/master/notebooks/02_Basics/09_Recurrent_Neural_Networks/09_TF_Recurrent_Neural_Networks.ipynb\"><img class=\"notebook-badge-image\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"uXuhm14WaCxy","colab_type":"text"},"source":["So far we've processed inputs as whole (ex. applying filters across the entire input to extract features) but we can also processed our inputs sequentially. To illustrate what this looks like, suppose our inputs are sentences (like the news dataset inputs we've seen in previous lessons). Think of each word/puncutation (or token) in the sentence as a timestep. So a sentence with 8 tokens has 8 timesteps. We can process each timestep, one at a time, and predict the class after the last timestep (token) has been processed. This is very powerful because the model now has a meaningful way to account for the order in our sequence and predict accordingly. "]},{"cell_type":"markdown","metadata":{"id":"m5JJY0tl8StF","colab_type":"text"},"source":["# Overview"]},{"cell_type":"markdown","metadata":{"id":"JqxyljU18hvt","colab_type":"text"},"source":["* **Objective:**  Process sequential data by accounting for the currend input and also what has been learned from previous inputs.\n","* **Advantages:** \n","    * Account for order and previous inputs in a meaningful way.\n","    * Conditioned generation for generating sequences.\n","* **Disadvantages:** \n","    * Each time step's prediction depends on the previous prediction so it's difficult to parallelize RNN operations. \n","    * Processing long sequences can yield memory and computation issues.\n","    * Interpretability is difficult but there are few [techniques](https://arxiv.org/abs/1506.02078) that use the activations from RNNs to see what parts of the inputs are processed. \n","* **Miscellaneous:** \n","    * Architectural tweaks to make RNNs faster and interpretable is an ongoing area of research."]},{"cell_type":"markdown","metadata":{"id":"nvrqNIJInP5S","colab_type":"text"},"source":["# Data"]},{"cell_type":"markdown","metadata":{"id":"c69z9wpJ56nE","colab_type":"text"},"source":["## Load data"]},{"cell_type":"markdown","metadata":{"id":"2V_nEp5G58M0","colab_type":"text"},"source":["We will download the [AG News dataset](http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html), which consists of 120000 text samples from 4 unique classes ('Business', 'Sci/Tech', 'Sports', 'World')"]},{"cell_type":"code","metadata":{"id":"y3qKSoEe57na","colab_type":"code","colab":{}},"source":["import numpy as np\n","import pandas as pd\n","import re\n","import urllib"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BqaHCtToXWAb","colab_type":"code","colab":{}},"source":["SEED = 1234\n","DATA_FILE = 'news.csv'\n","INPUT_FEATURE = 'title'\n","OUTPUT_FEATURE = 'category'"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"toxVrjXUXWPI","colab_type":"code","colab":{}},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cGQo98566GIV","colab_type":"code","colab":{}},"source":["# Load data from GitHub to this notebook's local drive\n","url = \"https://raw.githubusercontent.com/madewithml/lessons/master/data/news.csv\"\n","response = urllib.request.urlopen(url)\n","html = response.read()\n","with open(DATA_FILE, 'wb') as fp:\n","    fp.write(html)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dG_Oltib6G-9","colab_type":"code","outputId":"ee645dec-00b5-4903-8c6d-a08bf5598a73","executionInfo":{"status":"ok","timestamp":1583969940613,"user_tz":420,"elapsed":4496,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Load data\n","df = pd.read_csv(DATA_FILE, header=0)\n","X = df[INPUT_FEATURE].values\n","y = df[OUTPUT_FEATURE].values\n","df.head(5)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>title</th>\n","      <th>category</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n","      <td>Business</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n","      <td>Business</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n","      <td>Business</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n","      <td>Business</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Oil prices soar to all-time record, posing new...</td>\n","      <td>Business</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                               title  category\n","0  Wall St. Bears Claw Back Into the Black (Reuters)  Business\n","1  Carlyle Looks Toward Commercial Aerospace (Reu...  Business\n","2    Oil and Economy Cloud Stocks' Outlook (Reuters)  Business\n","3  Iraq Halts Oil Exports from Main Southern Pipe...  Business\n","4  Oil prices soar to all-time record, posing new...  Business"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"hxo6RKCQ71dl","colab_type":"text"},"source":["## Split data"]},{"cell_type":"code","metadata":{"id":"eS6kCcfY6IHE","colab_type":"code","colab":{}},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4qkWnO3uYPnk","colab_type":"code","colab":{}},"source":["TRAIN_SIZE = 0.7\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15\n","SHUFFLE = True"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0gbh2TLBXVDu","colab_type":"code","colab":{}},"source":["def train_val_test_split(X, y, val_size, test_size, shuffle):\n","    \"\"\"Split data into train/val/test datasets.\n","    \"\"\"\n","    X_train, X_test, y_train, y_test = train_test_split(\n","        X, y, test_size=test_size, stratify=y, shuffle=shuffle)\n","    X_train, X_val, y_train, y_val = train_test_split(\n","        X_train, y_train, test_size=val_size, stratify=y_train, shuffle=shuffle)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"kqiQd2j_76gP","colab_type":"code","outputId":"5ad5f0c3-f38b-4f45-fa6a-805688204510","executionInfo":{"status":"ok","timestamp":1583969953504,"user_tz":420,"elapsed":2173,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":102}},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n","class_counts = dict(collections.Counter(y))\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")\n","print (f\"Classes: {class_counts}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["X_train: (86700,), y_train: (86700,)\n","X_val: (15300,), y_val: (15300,)\n","X_test: (18000,), y_test: (18000,)\n","Sample point: PGA overhauls system for Ryder Cup points → Sports\n","Classes: {'Business': 30000, 'Sci/Tech': 30000, 'Sports': 30000, 'World': 30000}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dIfmW7vJ8Jx1","colab_type":"text"},"source":["## Tokenizer"]},{"cell_type":"code","metadata":{"id":"0V6v1yeVGxNg","colab_type":"code","outputId":"5dd87a56-784c-4f4a-f183-54d505baa77e","executionInfo":{"status":"ok","timestamp":1583970010521,"user_tz":420,"elapsed":52264,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":904}},"source":["# Use TF 2.0 (not 2.1) because of an issue with tf.gather_nd with generators...\n","!pip install tensorflow==2.0"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Collecting tensorflow==2.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/0f/7bd55361168bb32796b360ad15a25de6966c9c1beb58a8e30c01c8279862/tensorflow-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n","\u001b[K     |████████████████████████████████| 86.3MB 36kB/s \n","\u001b[?25hRequirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (0.2.2)\n","Collecting tensorboard<2.1.0,>=2.0.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/54/99b9d5d52d5cb732f099baaaf7740403e83fe6b0cedde940fabd2b13d75a/tensorboard-2.0.2-py3-none-any.whl (3.8MB)\n","\u001b[K     |████████████████████████████████| 3.8MB 40.1MB/s \n","\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.11.2)\n","Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.0.8)\n","Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (3.10.0)\n","Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.1.0)\n","Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.17.5)\n","Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.27.1)\n","Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (0.34.2)\n","Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (0.8.1)\n","Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (3.1.0)\n","Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (0.9.0)\n","Collecting tensorflow-estimator<2.1.0,>=2.0.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n","\u001b[K     |████████████████████████████████| 450kB 43.7MB/s \n","\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (0.1.8)\n","Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.12.0)\n","Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0) (1.1.0)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (45.2.0)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.7.2)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2.21.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.0.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (0.4.1)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.2.1)\n","Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0) (2.8.0)\n","Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (0.2.8)\n","Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.1.1)\n","Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (4.0)\n","Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2.8)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (2019.11.28)\n","Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.0.4)\n","Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.24.3)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (1.3.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (0.4.8)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0) (3.1.0)\n","Installing collected packages: tensorboard, tensorflow-estimator, tensorflow\n","  Found existing installation: tensorboard 1.15.0\n","    Uninstalling tensorboard-1.15.0:\n","      Successfully uninstalled tensorboard-1.15.0\n","  Found existing installation: tensorflow-estimator 1.15.1\n","    Uninstalling tensorflow-estimator-1.15.1:\n","      Successfully uninstalled tensorflow-estimator-1.15.1\n","  Found existing installation: tensorflow 1.15.0\n","    Uninstalling tensorflow-1.15.0:\n","      Successfully uninstalled tensorflow-1.15.0\n","Successfully installed tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ChhTN8WDY4Z5","colab_type":"code","outputId":"91e94b9e-85ce-4243-9b2a-7362b2d5e666","executionInfo":{"status":"ok","timestamp":1583970013875,"user_tz":420,"elapsed":54992,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["import tensorflow as tf\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","from tensorflow.keras.utils import to_categorical\n","import urllib\n","print (f\"tf version: {tf.__version__}\")\n","print(\"GPU Available: \", tf.test.is_gpu_available('GPU'))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["tf version: 2.0.0\n","GPU Available:  False\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ylpHSbcZYsXa","colab_type":"code","colab":{}},"source":["FILTERS = \"!\\\"'#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\"\n","LOWER = True\n","CHAR_LEVEL = False"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"bVgBSnFTfE5D","colab_type":"code","colab":{}},"source":["def decode(indices, tokenizer):\n","    \"\"\"Decode a list of indices into string.\"\"\"\n","    return \" \".join([tokenizer.index_word[index] for index in indices])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"WcscM_vL8KvP","colab_type":"code","colab":{}},"source":["# Input vectorizer\n","X_tokenizer = Tokenizer(\n","    filters=FILTERS, lower=LOWER, char_level=CHAR_LEVEL, oov_token='<UNK>')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xV2JgpOA8PwO","colab_type":"code","outputId":"6732f7a7-696c-4a9b-a67e-c48b671e7906","executionInfo":{"status":"ok","timestamp":1583970014438,"user_tz":420,"elapsed":53166,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Fit only on train data\n","X_tokenizer.fit_on_texts(X_train)\n","vocab_size = len(X_tokenizer.word_index) + 1\n","print (f\"# tokens: {vocab_size}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["# tokens: 29782\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ybb-YZSz8Qno","colab_type":"code","outputId":"f4490487-0ec9-4a26-c267-dae6545abfc6","executionInfo":{"status":"ok","timestamp":1583970014439,"user_tz":420,"elapsed":50849,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["# Convert text to sequence of tokens\n","original_text = X_train[0]\n","X_train = np.array(X_tokenizer.texts_to_sequences(X_train))\n","X_val = np.array(X_tokenizer.texts_to_sequences(X_val))\n","X_test = np.array(X_tokenizer.texts_to_sequences(X_test))\n","preprocessed_text = decode(X_train[0], X_tokenizer)\n","print (f\"{original_text} \\n\\t→ {preprocessed_text} \\n\\t→ {X_train[0]}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["PGA overhauls system for Ryder Cup points \n","\t→ pga overhauls system for ryder cup points \n","\t→ [2013, 7327, 467, 5, 702, 118, 1137]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ORGuhjCf8TKh","colab_type":"text"},"source":["## LabelEncoder"]},{"cell_type":"code","metadata":{"id":"7aBBgzkW8Rxv","colab_type":"code","colab":{}},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ckM_MnQi8UTH","colab_type":"code","colab":{}},"source":["# Output vectorizer\n","y_tokenizer = LabelEncoder()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0-FkxqCT8WUk","colab_type":"code","outputId":"ff5203f9-7e71-41a3-aadb-d3c9e4e7d4a4","executionInfo":{"status":"ok","timestamp":1583970014441,"user_tz":420,"elapsed":49120,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Fit on train data\n","y_tokenizer = y_tokenizer.fit(y_train)\n","classes = list(y_tokenizer.classes_)\n","print (f\"classes: {classes}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["classes: ['Business', 'Sci/Tech', 'Sports', 'World']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yrLHd1i_8XAJ","colab_type":"code","outputId":"8119bded-b5fd-4f8e-9a69-a76752d7c1c4","executionInfo":{"status":"ok","timestamp":1583970014441,"user_tz":420,"elapsed":48727,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Convert labels to tokens\n","y_train = y_tokenizer.transform(y_train)\n","y_val = y_tokenizer.transform(y_val)\n","y_test = y_tokenizer.transform(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["y_train[0]: 2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bJAR-FDUSP4q","colab_type":"code","outputId":"5fdcac2b-ce64-4d6b-8983-fe8aeab789f5","executionInfo":{"status":"ok","timestamp":1583970014442,"user_tz":420,"elapsed":48235,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# Class weights\n","counts = np.bincount(y_train)\n","class_weights = {i: 1.0/count for i, count in enumerate(counts)}\n","print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["class counts: [21675 21675 21675 21675],\n","class weights: {0: 4.61361014994233e-05, 1: 4.61361014994233e-05, 2: 4.61361014994233e-05, 3: 4.61361014994233e-05}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eoWQk0hO9bK2","colab_type":"text"},"source":["## Generators"]},{"cell_type":"code","metadata":{"id":"GVxnbzgW8X1V","colab_type":"code","colab":{}},"source":["import math\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","from tensorflow.keras.utils import Sequence"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"aSdfc0-cb9fc","colab_type":"code","colab":{}},"source":["BATCH_SIZE = 256"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1w6wVKJe9fxk","colab_type":"code","colab":{}},"source":["class DataGenerator(Sequence):\n","    \"\"\"Custom data loader.\"\"\"\n","    def __init__(self, X, y, batch_size, shuffle=False):\n","        self.X = X\n","        self.y = y\n","        self.batch_size = batch_size\n","        self.shuffle = shuffle\n","        self.on_epoch_end()\n","\n","    def __len__(self):\n","        \"\"\"# of batches.\"\"\"\n","        return math.ceil(len(self.X) / self.batch_size)\n","\n","    def __str__(self):\n","        return (f\"<DataGenerator(\" \\\n","                f\"batch_size={self.batch_size}, \" \\\n","                f\"batches={len(self)}, \" \\\n","                f\"shuffle={self.shuffle})>\")\n","\n","    def __getitem__(self, index):\n","        \"\"\"Generate a batch.\"\"\"\n","\n","        # Gather indices for this batch\n","        batch_indices = self.epoch_indices[index*self.batch_size:(index+1)*self.batch_size]\n","\n","        # Generate batch data\n","        inputs, outputs = self.create_batch(batch_indices=batch_indices)\n","\n","        return inputs, outputs\n","\n","    def on_epoch_end(self):\n","        \"\"\"Create indices after each epoch.\"\"\"\n","        self.epoch_indices = np.arange(len(self.X))\n","        if self.shuffle == True:\n","            np.random.shuffle(self.epoch_indices)\n","\n","    def create_batch(self, batch_indices):\n","        \"\"\"Generate data from indices.\"\"\"\n","        X = self.X[batch_indices]\n","        y = self.y[batch_indices]\n","        \n","        # Sequence lengths\n","        seq_lengths = np.array([[i, len(x)-1] for i, x in enumerate(X)])\n","\n","        # Pad batch\n","        max_seq_len = max([len(x) for x in X])\n","        X = pad_sequences(X, padding=\"post\", maxlen=max_seq_len)\n","\n","        return [X, seq_lengths], y"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5T8mVj9d9hNI","colab_type":"code","colab":{}},"source":["# Dataset generator\n","training_generator = DataGenerator(X=X_train,\n","                                   y=y_train,\n","                                   batch_size=BATCH_SIZE,\n","                                   shuffle=SHUFFLE)\n","validation_generator = DataGenerator(X=X_val,\n","                                     y=y_val,\n","                                     batch_size=BATCH_SIZE,\n","                                     shuffle=False)\n","testing_generator = DataGenerator(X=X_test,\n","                                  y=y_test,\n","                                  batch_size=BATCH_SIZE,\n","                                  shuffle=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"drbY5WDX9kcL","colab_type":"code","outputId":"2725876a-d4b8-48e0-c735-0d0ef33cd73a","executionInfo":{"status":"ok","timestamp":1583970014445,"user_tz":420,"elapsed":42794,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["print (f\"training_generator: {training_generator}\")\n","print (f\"validation_generator: {validation_generator}\")\n","print (f\"testing_generator: {testing_generator}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["training_generator: <DataGenerator(batch_size=256, batches=339, shuffle=True)>\n","validation_generator: <DataGenerator(batch_size=256, batches=60, shuffle=False)>\n","testing_generator: <DataGenerator(batch_size=256, batches=71, shuffle=False)>\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"I9sRkMVkhEM5","colab_type":"code","outputId":"6a7df71d-96fc-4d27-81c8-e1ce59c8bcb1","executionInfo":{"status":"ok","timestamp":1583970014446,"user_tz":420,"elapsed":40915,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["# Sample iteration through a generator\n","print (f\"num test batches: {len(testing_generator)}\")\n","for i, batch in enumerate(testing_generator):\n","    if i >= 10: break\n","    print (f\"batch {i} | X: {batch[0][0].shape}, seq_lengths: {batch[0][1].shape}, y: {batch[1].shape}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["num test batches: 71\n","batch 0 | X: (256, 17), seq_lengths: (256, 2), y: (256,)\n","batch 1 | X: (256, 19), seq_lengths: (256, 2), y: (256,)\n","batch 2 | X: (256, 20), seq_lengths: (256, 2), y: (256,)\n","batch 3 | X: (256, 18), seq_lengths: (256, 2), y: (256,)\n","batch 4 | X: (256, 20), seq_lengths: (256, 2), y: (256,)\n","batch 5 | X: (256, 19), seq_lengths: (256, 2), y: (256,)\n","batch 6 | X: (256, 17), seq_lengths: (256, 2), y: (256,)\n","batch 7 | X: (256, 18), seq_lengths: (256, 2), y: (256,)\n","batch 8 | X: (256, 19), seq_lengths: (256, 2), y: (256,)\n","batch 9 | X: (256, 18), seq_lengths: (256, 2), y: (256,)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GN_ezwx7vBUo","colab_type":"text"},"source":["# Inputs"]},{"cell_type":"markdown","metadata":{"id":"EwyHfs4JvC7F","colab_type":"text"},"source":["Inputs to RNNs are sequential like text or time-series."]},{"cell_type":"code","metadata":{"id":"pSCOyGL_vPu5","colab_type":"code","colab":{}},"source":["from tensorflow.keras.layers import Input"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"d3e6DbkwhXow","colab_type":"code","colab":{}},"source":["EMBEDDING_DIM = 100"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"JOs64ORxvBei","colab_type":"code","outputId":"6116aebb-db3a-4fb9-d196-d6dc021190dc","executionInfo":{"status":"ok","timestamp":1583970014447,"user_tz":420,"elapsed":36466,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Input\n","sequence_size = 8 # words per input\n","x = Input(shape=(sequence_size, EMBEDDING_DIM))\n","print (x)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Tensor(\"input_1:0\", shape=(None, 8, 100), dtype=float32)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bUMibnFtuq_i","colab_type":"text"},"source":["# Simple RNN"]},{"cell_type":"markdown","metadata":{"id":"Ufxzi5G0DScl","colab_type":"text"},"source":["<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/02_Basics/09_Recurrent_Neural_Networks/simple_rnn.png\" width=\"500\">\n","</div>\n","\n","RNN forward pass for a single time step $X_t$:\n","\n","$h_t = tanh(W_{hh}h_{t-1} + W_{xh}X_t+b_h)$\n","\n","*where*:\n","* $W_{hh}$ = hidden units weights| $\\in \\mathbb{R}^{HXH}$ ($H$ is the hidden dim)\n","* $h_{t-1}$ = previous timestep's hidden state $\\in \\mathbb{R}^{NXH}$\n","* $W_{xh}$ = input weights| $\\in \\mathbb{R}^{EXH}$\n","* $X_t$ = input at time step t | $\\in \\mathbb{R}^{NXE}$ ($N$ is the batch size, $E$ is the embedding dim)\n","* $b_h$ = hidden units bias $\\in \\mathbb{R}^{HX1}$\n","* $h_t$ = output from RNN for timestep $t$\n","\n","**NOTE**: At the first time step, the previous hidden state $h_{t-1}$ can either be a zero vector (unconditioned) or initialized (conditioned). If we are conditioning the RNN, the first hidden state $h_0$ can belong to a specific condition or we can concat the specific condition to the randomly initialized hidden vectors at each time step. More on this in the subsequent notebooks on RNNs."]},{"cell_type":"code","metadata":{"id":"_jJ5TmJ2ut3z","colab_type":"code","colab":{}},"source":["from tensorflow.keras.layers import SimpleRNN"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"K-Eb1HdRhnmu","colab_type":"code","colab":{}},"source":["RNN_HIDDEN_DIM = 128\n","DROPOUT_P = 0.1\n","RNN_DROPOUT_P = 0.1"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"z-O2Du78ut8J","colab_type":"code","outputId":"22291a7e-5ee2-4178-a43e-3642f1bf1e4a","executionInfo":{"status":"ok","timestamp":1583970016781,"user_tz":420,"elapsed":2302,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# RNN forward pass (many to one)\n","rnn = SimpleRNN(units=RNN_HIDDEN_DIM, \n","                dropout=DROPOUT_P, \n","                recurrent_dropout=RNN_DROPOUT_P,\n","                return_sequences=False, # only get the output from the last sequential input\n","                return_state=True)\n","output, hidden_state = rnn(x)\n","print (f\"output {output.shape}\")\n","print (f\"hidden {hidden_state.shape}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["output (None, 128)\n","hidden (None, 128)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"FqOtTdDx3Nz7","colab_type":"code","outputId":"54dcc096-4243-441f-c13f-d8c1f6af4a67","executionInfo":{"status":"ok","timestamp":1583970016782,"user_tz":420,"elapsed":2280,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["# RNN forward pass (many to many)\n","rnn = SimpleRNN(units=RNN_HIDDEN_DIM, \n","                dropout=DROPOUT_P, \n","                recurrent_dropout=RNN_DROPOUT_P,\n","                return_sequences=True, # get outputs from every item in sequential input\n","                return_state=True)\n","outputs, hidden_state = rnn(x)\n","print (f\"output {outputs.shape}\")\n","print (f\"hidden {hidden_state.shape}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["output (None, 8, 128)\n","hidden (None, 128)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mnUh3iRpJ2j8","colab_type":"text"},"source":["There are many different ways to use RNNs. So far we've processed our inputs one timestep at a time and we could either use the RNN's output at each time step or just use the final input timestep's RNN output. Let's look at a few other possibilities.\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/02_Basics/09_Recurrent_Neural_Networks/rnn_examples.png\" width=\"1000\">\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"pfhjWZRD94hK","colab_type":"text"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"zVmJGm8m-KIz","colab_type":"text"},"source":["**Simple RNN**"]},{"cell_type":"code","metadata":{"id":"T8oCutDJ-d1J","colab_type":"code","colab":{}},"source":["import tensorflow.keras.backend as K\n","from tensorflow.keras.layers import Dense\n","from tensorflow.keras.layers import Dropout\n","from tensorflow.keras.layers import Embedding\n","from tensorflow.keras.layers import Input\n","from tensorflow.keras.layers import Masking\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.utils import plot_model"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"qoBC0u8XpbVv","colab_type":"code","colab":{}},"source":["HIDDEN_DIM = 100"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UPP5ROd69mXC","colab_type":"code","colab":{}},"source":["class RNN(Model):\n","    def __init__(self, vocab_size, embedding_dim, rnn_cell, \n","                 hidden_dim, dropout_p, num_classes):\n","        super(RNN, self).__init__(name=\"rnn\")\n","\n","        # Embeddings\n","        self.embedding = Embedding(input_dim=vocab_size,\n","                                   output_dim=embedding_dim,\n","                                   mask_zero=True,\n","                                   trainable=True)\n","        \n","        # Masking\n","        self.mask = Masking(mask_value=0.) # to ignore padding from operations\n","        \n","        # RNN\n","        self.rnn = rnn_cell\n","        \n","        # FC layers\n","        self.fc1 = Dense(units=hidden_dim, activation='relu')\n","        self.dropout = Dropout(rate=dropout_p)\n","        self.fc2 = Dense(units=num_classes, activation='softmax')\n","\n","    def call(self, inputs, training=False):\n","        \"\"\"Forward pass.\"\"\"\n","        \n","        # Inputs\n","        x_in, seq_lengths = inputs\n","\n","        # Embed\n","        x_emb = self.embedding(x_in)\n","        \n","        # Masking \n","        z = self.mask(x_emb) # to ignore padding from operations\n","\n","        # RNN\n","        z, hidden_state = self.rnn(x_emb)\n","\n","        # Gather last relevant index\n","        z = tf.gather_nd(z, K.cast(seq_lengths, 'int32'))\n","\n","        # FC\n","        z = self.fc1(z)\n","        z = self.dropout(z, training=training)\n","        y_pred = self.fc2(z)\n","\n","        return y_pred\n","    \n","    def summary(self, x_in_shape, seq_lengths_shape):\n","        x_in = Input(shape=x_in_shape, name='X')\n","        seq_lengths = Input(shape=seq_lengths_shape, name='seq_lengths')\n","        inputs = [x_in, seq_lengths]\n","        summary = Model(inputs=inputs, outputs=self.call(inputs), name=self.name)\n","        return plot_model(summary, show_shapes=True) # forward pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"LH_z2FqUn5DR","colab_type":"code","colab":{}},"source":["# Simple RNN cell\n","simple_rnn = SimpleRNN(units=RNN_HIDDEN_DIM, \n","                       dropout=DROPOUT_P, \n","                       recurrent_dropout=RNN_DROPOUT_P,\n","                       return_sequences=True,\n","                       return_state=True) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8UpuaGvguJUc","colab_type":"code","colab":{}},"source":["# Initialize model\n","model = RNN(vocab_size=vocab_size,\n","            embedding_dim=EMBEDDING_DIM,\n","            rnn_cell=simple_rnn, # using simple rnn cell\n","            hidden_dim=HIDDEN_DIM,\n","            dropout_p=DROPOUT_P,\n","            num_classes=len(classes))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"zHpaUFBzn5VE","colab_type":"code","outputId":"d043df93-4793-4f6d-fb8e-27b6df664363","executionInfo":{"status":"ok","timestamp":1583970072503,"user_tz":420,"elapsed":4974,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":754}},"source":["# Summary\n","model.summary(x_in_shape=(10,), seq_lengths_shape=(2,))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABAsAAALhCAYAAAA5NS1pAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nYIdiAQAAAAAAsEOxAAAAAAAA2Pl/eQK7LOWJLcMAAAAASUVORK5CYII=\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"ADmeEdSZj56Y","colab_type":"text"},"source":["In our model, we want to use the RNN's output after the last relevant token in the sentence is processed. The last relevant token doesn't refer the <PAD> tokens but to the last actual word in the sentence and its index is different for each input in the batch.\n","\n","This is why we included a `seq_lengths` variable in our `create_batch` function in the `Generator` class. The seq_lengths are passed in to the forward pass as an input and we use [tf.gather_nd](https://www.tensorflow.org/api_docs/python/tf/gather_nd) to gather the last relevant hidden state (before padding starts). \n","\n","```python\n","def create_batch(self, batch_indices):\n","    ...\n","    # Sequence lengths\n","    seq_lengths = np.array([[i, len(x)-1] for i, x in enumerate(X)])\n","    ...\n","    return [X, seq_lengths], y\n","```\n","\n","Once we have the relevant hidden state extracted, we proceed to apply some fully-connected layers (with softmax) to generate the class probabilities."]},{"cell_type":"markdown","metadata":{"id":"S9ggYO6yHIm2","colab_type":"text"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"geKOPVzVK6S9","colab_type":"code","colab":{}},"source":["from tensorflow.keras.callbacks import Callback\n","from tensorflow.keras.callbacks import EarlyStopping\n","from tensorflow.keras.callbacks import ReduceLROnPlateau\n","from tensorflow.keras.callbacks import TensorBoard\n","from tensorflow.keras.losses import SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import SparseCategoricalAccuracy\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.optimizers import Adam\n","%load_ext tensorboard"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OfSBvenCiIce","colab_type":"code","colab":{}},"source":["NUM_LAYERS = 1\n","LEARNING_RATE = 1e-3\n","EARLY_STOPPING_CRITERIA = 3\n","NUM_EPOCHS = 5"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ucn3tYq1_sE1","colab_type":"code","colab":{}},"source":["# Compile\n","model.compile(optimizer=Adam(lr=LEARNING_RATE),\n","              loss=SparseCategoricalCrossentropy(),\n","              metrics=[SparseCategoricalAccuracy()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"X5ac-uJXb-F7","colab_type":"code","colab":{}},"source":["# Callbacks\n","callbacks = [EarlyStopping(monitor='val_loss', patience=EARLY_STOPPING_CRITERIA, verbose=1, mode='min'),\n","             ReduceLROnPlateau(patience=1, factor=0.1, verbose=0),\n","             TensorBoard(log_dir='tensorboard/simple_rnn', histogram_freq=1, update_freq='epoch')]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"657e77f7-f116-4489-dadb-dce1724072c3","executionInfo":{"status":"ok","timestamp":1583970392417,"user_tz":420,"elapsed":292515,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"id":"F-uKcZrABKVZ","colab":{"base_uri":"https://localhost:8080/","height":187}},"source":["# Training\n","training_history = model.fit_generator(generator=training_generator,\n","                                       epochs=NUM_EPOCHS,\n","                                       validation_data=validation_generator,\n","                                       callbacks=callbacks,\n","                                       shuffle=False,\n","                                       class_weight=class_weights,\n","                                       verbose=1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/5\n","339/339 [==============================] - 58s 170ms/step - loss: 5.7626e-05 - sparse_categorical_accuracy: 0.3150 - val_loss: 0.7964 - val_sparse_categorical_accuracy: 0.7154\n","Epoch 2/5\n","339/339 [==============================] - 59s 173ms/step - loss: 2.8695e-05 - sparse_categorical_accuracy: 0.7520 - val_loss: 0.4783 - val_sparse_categorical_accuracy: 0.8376\n","Epoch 3/5\n","339/339 [==============================] - 58s 171ms/step - loss: 1.9602e-05 - sparse_categorical_accuracy: 0.8548 - val_loss: 0.4265 - val_sparse_categorical_accuracy: 0.8516\n","Epoch 4/5\n","339/339 [==============================] - 58s 172ms/step - loss: 1.6127e-05 - sparse_categorical_accuracy: 0.8861 - val_loss: 0.4191 - val_sparse_categorical_accuracy: 0.8529\n","Epoch 5/5\n","339/339 [==============================] - 58s 170ms/step - loss: 1.3743e-05 - sparse_categorical_accuracy: 0.9032 - val_loss: 0.4064 - val_sparse_categorical_accuracy: 0.8581\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fylHduxTK-0N","colab_type":"code","outputId":"c2abb6ac-4b9a-4504-85c9-cccd9500485c","executionInfo":{"status":"ok","timestamp":1583970396234,"user_tz":420,"elapsed":295730,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Evaluation\n","testing_history = model.evaluate_generator(generator=testing_generator, verbose=1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["71/71 [==============================] - 4s 61ms/step - loss: 0.3940 - sparse_categorical_accuracy: 0.8632\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gaJD2YGpcTfT","colab_type":"code","outputId":"67d0128d-0c36-433d-e381-615cf690279b","executionInfo":{"status":"ok","timestamp":1583970401258,"user_tz":420,"elapsed":300171,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":17}},"source":["%tensorboard --logdir tensorboard"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["\n","    <div id=\"root\"></div>\n","    <script>\n","      (function() {\n","        window.TENSORBOARD_ENV = window.TENSORBOARD_ENV || {};\n","        window.TENSORBOARD_ENV[\"IN_COLAB\"] = true;\n","        document.querySelector(\"base\").href = \"https://localhost:6006\";\n","        function fixUpTensorboard(root) {\n","          const tftb = root.querySelector(\"tf-tensorboard\");\n","          // Disable the fragment manipulation behavior in Colab. Not\n","          // only is the behavior not useful (as the iframe's location\n","          // is not visible to the user), it causes TensorBoard's usage\n","          // of `window.replace` to navigate away from the page and to\n","          // the `localhost:<port>` URL specified by the base URI, which\n","          // in turn causes the frame to (likely) crash.\n","          tftb.removeAttribute(\"use-hash\");\n","        }\n","        function executeAllScripts(root) {\n","          // When `script` elements are inserted into the DOM by\n","          // assigning to an element's `innerHTML`, the scripts are not\n","          // executed. Thus, we manually re-insert these scripts so that\n","          // TensorBoard can initialize itself.\n","          for (const script of root.querySelectorAll(\"script\")) {\n","            const newScript = document.createElement(\"script\");\n","            newScript.type = script.type;\n","            newScript.textContent = script.textContent;\n","            root.appendChild(newScript);\n","            script.remove();\n","          }\n","        }\n","        function setHeight(root, height) {\n","          // We set the height dynamically after the TensorBoard UI has\n","          // been initialized. This avoids an intermediate state in\n","          // which the container plus the UI become taller than the\n","          // final width and cause the Colab output frame to be\n","          // permanently resized, eventually leading to an empty\n","          // vertical gap below the TensorBoard UI. It's not clear\n","          // exactly what causes this problematic intermediate state,\n","          // but setting the height late seems to fix it.\n","          root.style.height = `${height}px`;\n","        }\n","        const root = document.getElementById(\"root\");\n","        fetch(\".\")\n","          .then((x) => x.text())\n","          .then((html) => void (root.innerHTML = html))\n","          .then(() => fixUpTensorboard(root))\n","          .then(() => executeAllScripts(root))\n","          .then(() => setHeight(root, 800));\n","      })();\n","    </script>\n","  "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"lFWSgL8E5_HU","colab_type":"text"},"source":["# Bidirectional RNN"]},{"cell_type":"markdown","metadata":{"id":"Wa93OM6G1UVW","colab_type":"text"},"source":["We can also have RNNs that process inputs from both directions (first token to last token and vice versa) and combine their outputs. This architecture is known as a bidirectional RNN. "]},{"cell_type":"code","metadata":{"id":"sO_vP00z6BiJ","colab_type":"code","colab":{}},"source":["from tensorflow.keras.layers import Bidirectional"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"WqtGrVhm7914","colab_type":"code","colab":{}},"source":["class BiRNN(Model):\n","    def __init__(self, vocab_size, embedding_dim, rnn_cell, \n","                 hidden_dim, dropout_p, num_classes):\n","        super(BiRNN, self).__init__(name=\"biRNN\")\n","\n","        # Embeddings\n","        self.embedding = Embedding(input_dim=vocab_size,\n","                                   output_dim=embedding_dim,\n","                                   mask_zero=True,\n","                                   trainable=True)\n","        \n","        # Masking\n","        self.mask = Masking(mask_value=0.)\n","        \n","        # RNN\n","        self.rnn = Bidirectional(rnn_cell, merge_mode='sum')\n","        \n","        # FC layers\n","        self.fc1 = Dense(units=hidden_dim, activation='relu')\n","        self.dropout = Dropout(rate=dropout_p)\n","        self.fc2 = Dense(units=num_classes, activation='softmax')\n","\n","    def call(self, inputs, training=False):\n","        \"\"\"Forward pass.\"\"\"\n","        \n","        # Inputs\n","        x_in, seq_lengths = inputs\n","\n","        # Embed\n","        x_emb = self.embedding(x_in)\n","        \n","        # Masking\n","        z = self.mask(x_emb)\n","\n","        # RNN\n","        z, hidden_state_fw, hidden_state_bw = self.rnn(x_emb)\n","\n","        # Gather last relevant index\n","        z = tf.gather_nd(z, K.cast(seq_lengths, 'int32'))\n","\n","        # FC\n","        z = self.fc1(z)\n","        z = self.dropout(z, training=training)\n","        y_pred = self.fc2(z)\n","\n","        return y_pred\n","    \n","    def summary(self, x_in_shape, seq_lengths_shape):\n","        x_in = Input(shape=x_in_shape, name='X')\n","        seq_lengths = Input(shape=seq_lengths_shape, name='seq_lengths')\n","        inputs = [x_in, seq_lengths]\n","        summary = Model(inputs=inputs, outputs=self.call(inputs), name=self.name)\n","        return plot_model(summary, show_shapes=True) # forward pass"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ShsjY_5duYqm","colab_type":"code","colab":{}},"source":["# Simple RNN cell\n","simple_rnn = SimpleRNN(units=RNN_HIDDEN_DIM, \n","                       dropout=DROPOUT_P, \n","                       recurrent_dropout=RNN_DROPOUT_P,\n","                       return_sequences=True,\n","                       return_state=True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FkqYRBWd6OCR","colab_type":"code","colab":{}},"source":["# Initialize model\n","model = BiRNN(vocab_size=vocab_size,\n","              embedding_dim=EMBEDDING_DIM,\n","              rnn_cell=simple_rnn, # using simple rnn cell\n","              hidden_dim=HIDDEN_DIM,\n","              dropout_p=DROPOUT_P,\n","              num_classes=len(classes))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"W9ioEizbudtw","colab_type":"code","outputId":"e4545a28-80ee-4bb0-fd87-64dcb800f2e4","executionInfo":{"status":"ok","timestamp":1583970470536,"user_tz":420,"elapsed":2567,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":754}},"source":["# Summary\n","model.summary(x_in_shape=(10,), seq_lengths_shape=(2,))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABSoAAALhCAIAAADXRg0QAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\n5xb4AroAAAAASUVORK5CYII=\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"O_XtqY6n6Qdh","colab_type":"code","colab":{}},"source":["# Compile\n","model.compile(optimizer=Adam(lr=LEARNING_RATE),\n","              loss=SparseCategoricalCrossentropy(),\n","              metrics=[SparseCategoricalAccuracy()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"k07ilI7K6N_v","colab_type":"code","colab":{}},"source":["# Callbacks\n","callbacks = [EarlyStopping(monitor='val_loss', patience=EARLY_STOPPING_CRITERIA, verbose=1, mode='min'),\n","             ReduceLROnPlateau(patience=1, factor=0.1, verbose=0),\n","             TensorBoard(log_dir='tensorboard/simple_birnn', histogram_freq=1, update_freq='epoch')]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uuZwiGRt6X-w","colab_type":"code","outputId":"12ca38ca-24c3-430e-f06d-6f0fe33795b0","executionInfo":{"status":"ok","timestamp":1583270676650,"user_tz":480,"elapsed":1495866,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":340}},"source":["# Training\n","training_history = model.fit_generator(generator=training_generator,\n","                                       epochs=NUM_EPOCHS,\n","                                       validation_data=validation_generator,\n","                                       callbacks=callbacks,\n","                                       shuffle=False,\n","                                       class_weight=class_weights,\n","                                       verbose=1) "],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/10\n","339/339 [==============================] - 100s 296ms/step - loss: 5.6063e-05 - sparse_categorical_accuracy: 0.3227 - val_loss: 0.7378 - val_sparse_categorical_accuracy: 0.7328\n","Epoch 2/10\n","339/339 [==============================] - 96s 283ms/step - loss: 2.7235e-05 - sparse_categorical_accuracy: 0.7649 - val_loss: 0.4763 - val_sparse_categorical_accuracy: 0.8361\n","Epoch 3/10\n","339/339 [==============================] - 95s 281ms/step - loss: 1.9202e-05 - sparse_categorical_accuracy: 0.8574 - val_loss: 0.4254 - val_sparse_categorical_accuracy: 0.8510\n","Epoch 4/10\n","339/339 [==============================] - 98s 289ms/step - loss: 1.5746e-05 - sparse_categorical_accuracy: 0.8858 - val_loss: 0.4090 - val_sparse_categorical_accuracy: 0.8575\n","Epoch 5/10\n","339/339 [==============================] - 97s 285ms/step - loss: 1.3364e-05 - sparse_categorical_accuracy: 0.9061 - val_loss: 0.4132 - val_sparse_categorical_accuracy: 0.8560\n","Epoch 6/10\n","339/339 [==============================] - 97s 286ms/step - loss: 1.0407e-05 - sparse_categorical_accuracy: 0.9285 - val_loss: 0.4030 - val_sparse_categorical_accuracy: 0.8610\n","Epoch 7/10\n","339/339 [==============================] - 97s 286ms/step - loss: 9.8662e-06 - sparse_categorical_accuracy: 0.9316 - val_loss: 0.4077 - val_sparse_categorical_accuracy: 0.8613\n","Epoch 8/10\n","339/339 [==============================] - 98s 290ms/step - loss: 9.4388e-06 - sparse_categorical_accuracy: 0.9340 - val_loss: 0.4065 - val_sparse_categorical_accuracy: 0.8618\n","Epoch 9/10\n","339/339 [==============================] - 96s 284ms/step - loss: 9.3605e-06 - sparse_categorical_accuracy: 0.9359 - val_loss: 0.4065 - val_sparse_categorical_accuracy: 0.8618\n","Epoch 00009: early stopping\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"jR3trOIQcWY2","colab_type":"text"},"source":["# Gated RNNs: LSTMs & GRUs"]},{"cell_type":"markdown","metadata":{"id":"IqFsufNVch4W","colab_type":"text"},"source":["While our simple RNNs so far are great for sequentially processing our inputs, they have quite a few disadvantages. They commonly suffer from exploding or vanishing gradients as a result using the same set of weights ($W_{xh}$ and $W_{hh}$) with each timestep's input. During backpropagation, this can cause gradients to explode (>1) or vanish (<1). If you multiply any number greater than 1 with itself over and over, it moves towards infinity (exploding gradients) and similarly,  If you multiply any number less than 1 with itself over and over, it moves towards zero (vanishing gradients). To mitigate this issue, gated RNNs were devised to selectively retrain information. If you're interested in learning more of the specifics, this [post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) is a must-read.\n","\n","There are two popular types of gated RNNs: Long Short-term Memory (LSTMs) units and Gated Recurrent Units (GRUs).\n","\n","<div align=\"left\">\n","<img src=\"https://raw.githubusercontent.com/madewithml/images/master/02_Basics/09_Recurrent_Neural_Networks/gated_rnns.png\" width=\"600\">\n","</div>\n","<a href=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/\">Understanding LSTM Networks</a> - Chris Olah\n","\n","\n","**NOTE**: When deciding between LSTMs and GRUs, empirical performance is the best factor but in genreal GRUs offer similar performance with less complexity (less weights). \n","\n"]},{"cell_type":"code","metadata":{"id":"PqVCqWv2fz4N","colab_type":"code","colab":{}},"source":["from tensorflow.keras.layers import GRU"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7tBHovnxcTwV","colab_type":"code","colab":{}},"source":["# RNN cell\n","gru = GRU(units=RNN_HIDDEN_DIM, \n","          dropout=DROPOUT_P, \n","          recurrent_dropout=RNN_DROPOUT_P,\n","          return_sequences=True,\n","          return_state=True) "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"n7BfnoAbcTtk","colab_type":"code","colab":{}},"source":["# Initialize model\n","model = BiRNN(vocab_size=vocab_size,\n","              embedding_dim=EMBEDDING_DIM,\n","              rnn_cell=gru, # use gru cell\n","              hidden_dim=HIDDEN_DIM,\n","              dropout_p=DROPOUT_P,\n","              num_classes=len(classes))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"HCkdFxuGugSz","colab_type":"code","outputId":"dfd0b48f-c822-430b-de26-b9ef92518c2e","executionInfo":{"status":"ok","timestamp":1583970526515,"user_tz":420,"elapsed":4455,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":754}},"source":["# Summary\n","model.summary(x_in_shape=(10,), seq_lengths_shape=(2,))"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABLcAAALhCAIAAAAindGJAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nAABAH1SJAAAAAAAA0Of/AN5ejy896LHVAAAAAElFTkSuQmCC\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":63}]},{"cell_type":"code","metadata":{"id":"Vd8SL8UIcTqc","colab_type":"code","colab":{}},"source":["# Compile\n","model.compile(optimizer=Adam(lr=LEARNING_RATE),\n","              loss=SparseCategoricalCrossentropy(),\n","              metrics=[SparseCategoricalAccuracy()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"c8dNn6VPcTkx","colab_type":"code","colab":{}},"source":["# Callbacks\n","callbacks = [EarlyStopping(monitor='val_loss', patience=EARLY_STOPPING_CRITERIA, verbose=1, mode='min'),\n","             ReduceLROnPlateau(patience=1, factor=0.1, verbose=0),\n","             TensorBoard(log_dir='tensorboard/gru', histogram_freq=1, update_freq='epoch')]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"NbpjRVficTcY","colab_type":"code","outputId":"fc73848d-326d-4cdb-a6bc-200d2d6895b2","executionInfo":{"status":"ok","timestamp":1583272889729,"user_tz":480,"elapsed":2145336,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# Training\n","training_history = model.fit_generator(generator=training_generator,\n","                                       epochs=NUM_EPOCHS,\n","                                       validation_data=validation_generator,\n","                                       callbacks=callbacks,\n","                                       shuffle=False,\n","                                       class_weight=class_weights,\n","                                       verbose=1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/10\n","339/339 [==============================] - 207s 610ms/step - loss: 6.3519e-05 - sparse_categorical_accuracy: 0.2851 - val_loss: 1.3547 - val_sparse_categorical_accuracy: 0.3324\n","Epoch 2/10\n","339/339 [==============================] - 210s 618ms/step - loss: 5.3265e-05 - sparse_categorical_accuracy: 0.4139 - val_loss: 0.7810 - val_sparse_categorical_accuracy: 0.7101\n","Epoch 3/10\n","339/339 [==============================] - 213s 629ms/step - loss: 2.6807e-05 - sparse_categorical_accuracy: 0.7586 - val_loss: 0.4960 - val_sparse_categorical_accuracy: 0.8286\n","Epoch 4/10\n","339/339 [==============================] - 217s 642ms/step - loss: 1.9131e-05 - sparse_categorical_accuracy: 0.8560 - val_loss: 0.4458 - val_sparse_categorical_accuracy: 0.8473\n","Epoch 5/10\n","339/339 [==============================] - 221s 652ms/step - loss: 1.5748e-05 - sparse_categorical_accuracy: 0.8913 - val_loss: 0.4224 - val_sparse_categorical_accuracy: 0.8532\n","Epoch 6/10\n","339/339 [==============================] - 215s 634ms/step - loss: 1.3543e-05 - sparse_categorical_accuracy: 0.9067 - val_loss: 0.4188 - val_sparse_categorical_accuracy: 0.8548\n","Epoch 7/10\n","339/339 [==============================] - 215s 634ms/step - loss: 1.2065e-05 - sparse_categorical_accuracy: 0.9199 - val_loss: 0.4228 - val_sparse_categorical_accuracy: 0.8547\n","Epoch 8/10\n","339/339 [==============================] - 215s 635ms/step - loss: 9.6303e-06 - sparse_categorical_accuracy: 0.9333 - val_loss: 0.4150 - val_sparse_categorical_accuracy: 0.8587\n","Epoch 9/10\n","339/339 [==============================] - 215s 635ms/step - loss: 9.2305e-06 - sparse_categorical_accuracy: 0.9374 - val_loss: 0.4165 - val_sparse_categorical_accuracy: 0.8586\n","Epoch 10/10\n","339/339 [==============================] - 217s 639ms/step - loss: 8.8766e-06 - sparse_categorical_accuracy: 0.9404 - val_loss: 0.4169 - val_sparse_categorical_accuracy: 0.8586\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"4ytG83xxcTZp","colab_type":"code","outputId":"7bcaa74e-d6be-4fb4-d31f-fe6506c1adab","executionInfo":{"status":"ok","timestamp":1583272904589,"user_tz":480,"elapsed":2156780,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Evaluation\n","testing_history = model.evaluate_generator(generator=testing_generator, verbose=1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["71/71 [==============================] - 14s 203ms/step - loss: 0.4063 - sparse_categorical_accuracy: 0.8605\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"0eXhh4rqcTXE","colab_type":"code","outputId":"0875e1e1-5949-49e0-c4c5-5a3f322834f1","executionInfo":{"status":"ok","timestamp":1583272904590,"user_tz":480,"elapsed":2156345,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["%tensorboard --logdir tensorboard"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/plain":["Reusing TensorBoard on port 6006 (pid 401), started 0:51:45 ago. (Use '!kill 401' to kill it.)"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["\n","    <div id=\"root\"></div>\n","    <script>\n","      (function() {\n","        window.TENSORBOARD_ENV = window.TENSORBOARD_ENV || {};\n","        window.TENSORBOARD_ENV[\"IN_COLAB\"] = true;\n","        document.querySelector(\"base\").href = \"https://localhost:6006\";\n","        function fixUpTensorboard(root) {\n","          const tftb = root.querySelector(\"tf-tensorboard\");\n","          // Disable the fragment manipulation behavior in Colab. Not\n","          // only is the behavior not useful (as the iframe's location\n","          // is not visible to the user), it causes TensorBoard's usage\n","          // of `window.replace` to navigate away from the page and to\n","          // the `localhost:<port>` URL specified by the base URI, which\n","          // in turn causes the frame to (likely) crash.\n","          tftb.removeAttribute(\"use-hash\");\n","        }\n","        function executeAllScripts(root) {\n","          // When `script` elements are inserted into the DOM by\n","          // assigning to an element's `innerHTML`, the scripts are not\n","          // executed. Thus, we manually re-insert these scripts so that\n","          // TensorBoard can initialize itself.\n","          for (const script of root.querySelectorAll(\"script\")) {\n","            const newScript = document.createElement(\"script\");\n","            newScript.type = script.type;\n","            newScript.textContent = script.textContent;\n","            root.appendChild(newScript);\n","            script.remove();\n","          }\n","        }\n","        function setHeight(root, height) {\n","          // We set the height dynamically after the TensorBoard UI has\n","          // been initialized. This avoids an intermediate state in\n","          // which the container plus the UI become taller than the\n","          // final width and cause the Colab output frame to be\n","          // permanently resized, eventually leading to an empty\n","          // vertical gap below the TensorBoard UI. It's not clear\n","          // exactly what causes this problematic intermediate state,\n","          // but setting the height late seems to fix it.\n","          root.style.height = `${height}px`;\n","        }\n","        const root = document.getElementById(\"root\");\n","        fetch(\".\")\n","          .then((x) => x.text())\n","          .then((html) => void (root.innerHTML = html))\n","          .then(() => fixUpTensorboard(root))\n","          .then(() => executeAllScripts(root))\n","          .then(() => setHeight(root, 800));\n","      })();\n","    </script>\n","  "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"vskwiiI3V3S6","colab_type":"text"},"source":["# Evaluation"]},{"cell_type":"code","metadata":{"id":"Itq7lT9qV9Y8","colab_type":"code","colab":{}},"source":["import io\n","import itertools\n","import json\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_fscore_support"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VT9C8rUyWAWF","colab_type":"code","colab":{}},"source":["def plot_confusion_matrix(y_true, y_pred, classes, cmap=plt.cm.Blues):\n","    \"\"\"Plot a confusion matrix using ground truth and predictions.\"\"\"\n","    # Confusion matrix\n","    cm = confusion_matrix(y_true, y_pred)\n","    cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","\n","    #  Figure\n","    fig = plt.figure()\n","    ax = fig.add_subplot(111)\n","    cax = ax.matshow(cm, cmap=plt.cm.Blues)\n","    fig.colorbar(cax)\n","\n","    # Axis\n","    plt.title(\"Confusion matrix\")\n","    plt.ylabel(\"True label\")\n","    plt.xlabel(\"Predicted label\")\n","    ax.set_xticklabels([''] + classes)\n","    ax.set_yticklabels([''] + classes)\n","    ax.xaxis.set_label_position('bottom') \n","    ax.xaxis.tick_bottom()\n","\n","    # Values\n","    thresh = cm.max() / 2.\n","    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n","        plt.text(j, i, f\"{cm[i, j]:d} ({cm_norm[i, j]*100:.1f}%)\",\n","                 horizontalalignment=\"center\",\n","                 color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n","    # Display\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OyguCCS3W6DO","colab_type":"code","colab":{}},"source":["def get_performance(y_true, y_pred, classes):\n","    \"\"\"Per-class performance metrics.\"\"\"\n","    performance = {'overall': {}, 'class': {}}\n","    y_pred = np.argmax(y_pred, axis=1)\n","    metrics = precision_recall_fscore_support(y_true, y_pred)\n","\n","    # Overall performance\n","    performance['overall']['precision'] = np.mean(metrics[0])\n","    performance['overall']['recall'] = np.mean(metrics[1])\n","    performance['overall']['f1'] = np.mean(metrics[2])\n","    performance['overall']['num_samples'] = np.float64(np.sum(metrics[3]))\n","\n","    # Per-class performance\n","    for i in range(len(classes)):\n","        performance['class'][classes[i]] = {\n","            \"precision\": metrics[0][i],\n","            \"recall\": metrics[1][i],\n","            \"f1\": metrics[2][i],\n","            \"num_samples\": np.float64(metrics[3][i])\n","        }\n","\n","    return performance"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"MHfGZh7JZGe6","colab_type":"code","outputId":"4904f708-764a-40e3-d633-1bdd05af37e6","executionInfo":{"status":"ok","timestamp":1583273022895,"user_tz":480,"elapsed":29440,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"source":["# Evaluation\n","test_history = model.evaluate_generator(generator=testing_generator, verbose=1)\n","y_pred = model.predict_generator(generator=testing_generator, verbose=1)\n","print (f\"test history: {test_history}\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["71/71 [==============================] - 15s 207ms/step - loss: 0.4063 - sparse_categorical_accuracy: 0.8605\n","71/71 [==============================] - 14s 201ms/step\n","test history: [0.40631949565779996, 0.8605]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"qdAj6KyCU88E","colab_type":"code","outputId":"0fec4551-84a5-4b55-e0b8-b29ecd4cf7aa","executionInfo":{"status":"ok","timestamp":1583273039065,"user_tz":480,"elapsed":430,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":595}},"source":["# Class performance\n","performance = get_performance(y_true=y_test, y_pred=y_pred, classes=classes)\n","print (json.dumps(performance, indent=4))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["{\n","    \"overall\": {\n","        \"precision\": 0.8608457987383706,\n","        \"recall\": 0.8604999999999999,\n","        \"f1\": 0.8605275947214777,\n","        \"num_samples\": 18000.0\n","    },\n","    \"class\": {\n","        \"Business\": {\n","            \"precision\": 0.8306023029229407,\n","            \"recall\": 0.8335555555555556,\n","            \"f1\": 0.8320763087843833,\n","            \"num_samples\": 4500.0\n","        },\n","        \"Sci/Tech\": {\n","            \"precision\": 0.826889661164205,\n","            \"recall\": 0.846,\n","            \"f1\": 0.836335676625659,\n","            \"num_samples\": 4500.0\n","        },\n","        \"Sports\": {\n","            \"precision\": 0.8991688538932633,\n","            \"recall\": 0.9135555555555556,\n","            \"f1\": 0.906305114638448,\n","            \"num_samples\": 4500.0\n","        },\n","        \"World\": {\n","            \"precision\": 0.8867223769730733,\n","            \"recall\": 0.8488888888888889,\n","            \"f1\": 0.8673932788374206,\n","            \"num_samples\": 4500.0\n","        }\n","    }\n","}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nRbPfqgZWaof","colab_type":"code","outputId":"c84b86e0-96b9-4a22-8a42-fd96deed8591","executionInfo":{"status":"ok","timestamp":1583273040093,"user_tz":480,"elapsed":787,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":598}},"source":["# Confusion matrix\n","plt.rcParams[\"figure.figsize\"] = (7,7)\n","y_pred = np.argmax(y_pred, axis=1)\n","plot_confusion_matrix(y_test, y_pred, classes=classes)\n","print (classification_report(y_test, y_pred))"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAc8AAAGKCAYAAABq7cr0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nylMppZSKIa08lVJKqRjSylMppZSKof8DkeFpOosxx/wAAAAASUVORK5CYII=\n","text/plain":["<Figure size 504x504 with 2 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.83      0.83      0.83      4500\n","           1       0.83      0.85      0.84      4500\n","           2       0.90      0.91      0.91      4500\n","           3       0.89      0.85      0.87      4500\n","\n","    accuracy                           0.86     18000\n","   macro avg       0.86      0.86      0.86     18000\n","weighted avg       0.86      0.86      0.86     18000\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yeiD1T_QZpdk","colab_type":"text"},"source":["# Inference"]},{"cell_type":"code","metadata":{"id":"z7G7vuSTZHkQ","colab_type":"code","colab":{}},"source":["import collections"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1GiJpWeZZwkp","colab_type":"code","colab":{}},"source":["def get_probability_distribution(y_prob, classes):\n","    results = {}\n","    for i, class_ in enumerate(classes):\n","        results[class_] = np.float64(y_prob[i])\n","    sorted_results = {k: v for k, v in sorted(\n","        results.items(), key=lambda item: item[1], reverse=True)}\n","    return sorted_results"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CLP2Vzp3Zwth","colab_type":"code","outputId":"d5ddd5ec-2ce8-42e3-e1bc-7bf892faef37","executionInfo":{"status":"ok","timestamp":1583273050856,"user_tz":480,"elapsed":274,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# Inputs\n","texts = [\"This weekend the greatest tennis players will fight for the championship.\"]\n","num_samples = len(texts)\n","X_infer = np.array(X_tokenizer.texts_to_sequences(texts))\n","print (f\"{texts[0]} \\n\\t→ {decode(X_infer[0], X_tokenizer)} \\n\\t→ {X_infer[0]}\")\n","print (f\"len(X_infer[0]): {len(X_infer[0])} characters\")\n","y_filler = np.array([0]*num_samples)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["This weekend the greatest tennis players will fight for the championship. \n","\t→ this weekend the greatest tennis players will fight for the championship \n","\t→ [ 261 2226   10 5941  916  388   64  232    5   10 1523]\n","len(X_infer[0]): 11 characters\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"q1gFlI5MZ143","colab_type":"code","colab":{}},"source":["# Inference data generator\n","inference_generator = DataGenerator(\n","    X=X_infer, y=y_filler, batch_size=BATCH_SIZE, shuffle=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"UFE4sp_7aHTq","colab_type":"code","outputId":"e7ac33bf-3755-4d1a-a0d8-4e46585c0673","executionInfo":{"status":"ok","timestamp":1583273052428,"user_tz":480,"elapsed":556,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# Predict\n","y_prob = model.predict_generator(generator=inference_generator, verbose=1)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\r1/1 [==============================] - 0s 49ms/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bGi_NvbBaMap","colab_type":"code","outputId":"7a174740-39fd-4eb8-9f3e-614e1b643120","executionInfo":{"status":"ok","timestamp":1583273053802,"user_tz":480,"elapsed":712,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"colab":{"base_uri":"https://localhost:8080/","height":238}},"source":["# Results\n","results = []\n","for index in range(num_samples):\n","    results.append({\n","        'raw_input': texts[index],\n","        'preprocessed_input': decode(indices=X_infer[index], tokenizer=X_tokenizer),\n","        'tokenized_input': str(X_infer[index]),\n","        'probabilities': get_probability_distribution(y_prob[index], y_tokenizer.classes_)\n","                   })\n","print (json.dumps(results, indent=4))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[\n","    {\n","        \"raw_input\": \"This weekend the greatest tennis players will fight for the championship.\",\n","        \"preprocessed_input\": \"this weekend the greatest tennis players will fight for the championship\",\n","        \"tokenized_input\": \"[ 261 2226   10 5941  916  388   64  232    5   10 1523]\",\n","        \"probabilities\": {\n","            \"Sports\": 0.997166097164154,\n","            \"World\": 0.0028288678731769323,\n","            \"Sci/Tech\": 4.848130629397929e-06,\n","            \"Business\": 1.7172565947021212e-07\n","        }\n","    }\n","]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4xknbwa-bW3U","colab_type":"text"},"source":["**NOTE**: We will learn how to get a little bit of interpretabiltiy with RNNs in the next lesson on attentional interfaces."]},{"cell_type":"markdown","metadata":{"id":"2I4XMUfsILku","colab_type":"text"},"source":["---\n","Share and discover ML projects at <a href=\"https://madewithml.com/\">Made With ML</a>.\n","\n","<div align=\"left\">\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://github.com/madewithml/lessons\"><img src=\"https://img.shields.io/github/stars/madewithml/lessons.svg?style=social&label=Star\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://www.linkedin.com/company/madewithml\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","<a class=\"ai-header-badge\" target=\"_blank\" href=\"https://twitter.com/madewithml\"><img src=\"https://img.shields.io/twitter/follow/madewithml.svg?label=Follow&style=social\"></a>\n","</div>\n","             "]}]}